fuente : https://www.ecdc.europa.eu/

Fecha actualización: 31/12/2019

df.dtypes

dateRep                    datetime64[ns]
day                                 int64
month                               int64
year                                int64
cases                               int64
deaths                              int64
countriesAndTerritories            object
geoId                              object
countryterritoryCode               object
popData2018                       float64
continentExp                       object
min_date                   datetime64[ns]
days                              float64
dtype: object

#collapse-hide
df.head(10)
pais dateRep day month year cases deaths geoId countryterritoryCode popData2018 continentExp min_date days cum_cases cum_deaths date_int max_cases
13 China 2019-12-31 31.0 12.0 2019.0 27.0 0.0 CN CHN 1.392730e+09 Asia 2019-12-31 0.0 27.0 0.0 20191231.0 84038.0
80 China 2020-01-01 1.0 1.0 2020.0 0.0 0.0 CN CHN 1.392730e+09 Asia 2019-12-31 1.0 27.0 0.0 20200101.0 84038.0
197 United_Arab_Emirates 2020-02-01 2.0 1.0 2020.0 0.0 0.0 AE ARE 9.630959e+06 Asia 2020-01-03 29.0 611.0 8.0 20200102.0 21831.0
134 Afghanistan 2020-02-01 2.0 1.0 2020.0 0.0 0.0 AF AFG 3.717239e+07 Asia 2020-01-04 28.0 247.0 4.0 20200102.0 6402.0
136 Armenia 2020-02-01 2.0 1.0 2020.0 0.0 0.0 AM ARM 2.951776e+06 Europe 2020-01-03 29.0 185.0 2.0 20200102.0 4044.0
138 Austria 2020-02-01 2.0 1.0 2020.0 0.0 0.0 AT AUT 8.847037e+06 Europe 2020-01-03 29.0 627.0 24.0 20200102.0 16068.0
137 Australia 2020-02-01 2.0 1.0 2020.0 0.0 0.0 AU AUS 2.499237e+07 Oceania 2020-01-02 30.0 176.0 4.0 20200102.0 7019.0
139 Azerbaijan 2020-02-01 2.0 1.0 2020.0 0.0 0.0 AZ AZE 9.942334e+06 Europe 2020-01-04 28.0 63.0 2.0 20200102.0 2980.0
142 Belgium 2020-02-01 2.0 1.0 2020.0 0.0 0.0 BE BEL 1.142207e+07 Europe 2020-01-04 28.0 1536.0 285.0 20200102.0 54644.0
140 Bahrain 2020-02-01 2.0 1.0 2020.0 0.0 0.0 BH BHR 1.569439e+06 Asia 2020-01-03 29.0 174.0 0.0 20200102.0 6583.0

Casos acumulados por país

Evolución Diaria 

#collapse-hide
source = df[df['max_cases'] >= 10000]
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)



alt.data_transformers.enable('default', max_rows=None)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('dateRep:T'),
    alt.Y('cum_cases:Q', scale=alt.Scale(type='log')),
    alt.Color('pais:N')
)

alt.layer(
    base, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='dateRep:T'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=5).encode(
        text='cum_cases:Q'
    ).transform_filter(label),
    
    base.mark_text(align='left', dx=35, dy=-5, stroke='white', strokeWidth=5).encode(
        text='pais:N'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='cum_cases:Q'
    ).transform_filter(label),
    
    base.mark_text(align='left', dx=35, dy=-5).encode(
        text='pais:N'
    ).transform_filter(label),
    
    data=source
).properties(
    width=700,
    height=400

)

Evolución de casos detectados por día del primer caso detectado en cada país 

#collapse-hide
source = df[df['max_cases'] >= 10000]
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

alt.data_transformers.enable('default', max_rows=None)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('days:Q'),
    alt.Y('cum_cases:Q', scale=alt.Scale(type='log')),
    alt.Color('pais:N')
)

alt.layer(
    base, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='days:Q'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=2).encode(
        text='cum_cases:Q'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='cum_cases:Q'
    ).transform_filter(label),
    
    base.mark_text(align='left', dx=40, dy=-5).encode(
        text='pais:N'
    ).transform_filter(label),
    
    data=source
).properties(
    width=700,
    height=400
)

Escala logaritmica de casos y casos acumulados por pais 

#collapse-hide
source = df[df['cases'] > 0]
source = source[source['max_cases'] >= 30000]
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

alt.data_transformers.enable('default', max_rows=None)

# define our base line chart of stock prices
base = alt.Chart().mark_line(point=True).encode(
    alt.X('cum_cases:Q', axis=alt.Axis(grid=False), scale=alt.Scale(type='log')),
    alt.Y('cases:Q', axis=alt.Axis(grid=False), scale=alt.Scale(type='log')),
    alt.Color('pais:N')
)

alt.layer(
    base, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='cum_cases:Q'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=2).encode(
        text='cum_cases:Q'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='cum_cases:Q'
    ).transform_filter(label),
    
    base.mark_text(align='left', dx=40, dy=-5).encode(
        text='pais:N'
    ).transform_filter(label),
    
    data=source
).properties(
    width=700,
    height=400
)

Evolución de desesos detectados por día del primer caso detectado en cada país 

#collapse-hide
source = df#.where(df['max_cases'] >= 1000)
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

alt.data_transformers.enable('default', max_rows=None)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('days:Q'),
    alt.Y('cum_deaths:Q'),
    alt.Color('pais:N')
)

alt.layer(
    base, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='days:Q'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=2).encode(
        text='cum_deaths:Q'
    ).transform_filter(label),
    
    base.mark_text(align='left', dx=37, dy=-5, stroke='white', strokeWidth=2).encode(
        text='pais:N'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='cum_deaths:Q'
    ).transform_filter(label),
    
    base.mark_text(align='left', dx=37, dy=-5).encode(
        text='pais:N'
    ).transform_filter(label),
    
    data=source
).properties(
    width=700,
    height=400
)

Evolución de desesos detectados por fecha en cada país 

#collapse-hide
source = df#.where(df['max_cases'] >= 1000)
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

alt.data_transformers.enable('default', max_rows=None)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('dateRep:T'),
    alt.Y('cum_deaths:Q'),
    alt.Color('pais:N')
)

alt.layer(
    base, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='dateRep:T'
    ).transform_filter(label),
    
    
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='right', dx=5, dy=-5, stroke='white', strokeWidth=5).encode(
        text='cum_deaths:Q'
    ).transform_filter(label),
    
    base.mark_text(align='right', dx=5, dy=-15, stroke='white', strokeWidth=5).encode(
        text='pais:N'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='right', dx=5, dy=-5).encode(
        text='cum_deaths:Q'
    ).transform_filter(label),
    
    base.mark_text(align='right', dx=5, dy=-15).encode(
        text='pais:N'
    ).transform_filter(label),
    
    data=source
).properties(
    width=700,
    height=400
).interactive()

Casos acumulados y desesos acumulados del último día 

#collapse-hide
date = data['dateRep'].max()
source = df.where(df['dateRep'] >= date)
source = source.where(source['max_cases'] >= 1000)


alt.Chart(source).mark_circle(size=100).encode(
    alt.X('cum_cases:Q'),
    alt.Y('cum_deaths:Q'),
    color='pais',
    tooltip=['pais:N', 'cum_deaths:Q', 'cum_cases:Q']
).properties(
    width=700,
    height=400
).interactive()

Relacion de casos detectados y desesos por pais

Argentina

Curva de casos acumulados por día 

#collapse-hide
source = df.where(df['pais'] == 'Argentina')
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('days:Q'),
    alt.Y('cum_cases:Q', scale=alt.Scale(type='log')),
    alt.Color('pais:N')
)

overlay = pd.DataFrame({'cuarentena': [13]})
vline = alt.Chart(overlay).mark_rule(color='red', strokeWidth=1).encode(x='cuarentena:Q')

alt.layer(
    base, vline,# base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='days:Q'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=5).encode(
        text='cum_cases:Q'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='cum_cases:Q'
    ).transform_filter(label),
    
    data=source
      
).properties(
    width=700,
    height=400 )

# La línea roja marca el inicio de la cuarentena


#collapse-hide
source = df.query('pais in ["Argentina", "Chile", "Brazil", "Spain"]')
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('days:Q'),
    alt.Y('cum_cases:Q', scale=alt.Scale(type='log')),
    alt.Color('pais:N')
)

overlay = pd.DataFrame({'cuarentena': [13]})
vline = alt.Chart(overlay).mark_rule(color='red', strokeWidth=1).encode(x='cuarentena:Q')

alt.layer(
    base, vline,# base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='days:Q'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=5).encode(
        text='cum_cases:Q'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='cum_cases:Q'
    ).transform_filter(label),
    
    data=source
      
).properties(
    width=700,
    height=400 )

# La línea roja marca el inicio de la cuarentena


#collapse-hide
source = df.where(df['pais'] == 'Argentina')
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('days:Q'),
    alt.Y('cum_deaths:Q'),
    alt.Color('pais:N')
)

overlay = pd.DataFrame({'cuarentena': [13]})
vline = alt.Chart(overlay).mark_rule(color='red', strokeWidth=1).encode(x='cuarentena:Q')

alt.layer(
    base, vline, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='days:Q'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=2).encode(
        text='cum_deaths:Q'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='cum_deaths:Q'
    ).transform_filter(label),
    
    data=source
).properties(
    width=700,
    height=400
)


# La línea roja marca el inicio de la cuarentena

Casos detectados por día 

#collapse-hide
source = df.where(df['pais'] == 'Argentina')
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('dateRep:T'),
    alt.Y('cases:Q'),
    alt.Color('pais:N'),
    tooltip=['pais:N', 'dateRep:T', 'cum_cases:Q']
)

overlay = pd.DataFrame({'cuarentena': ['2020-03-17']})
vline = alt.Chart(overlay).mark_rule(color='red', strokeWidth=1).encode(x='cuarentena:T')

alt.layer(
    base, vline, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='dateRep:T'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=2).encode(
        text='cases:Q'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='cases:Q'
    ).transform_filter(label),
    
    data=source
).properties(
    width=700,
    height=400
)


# La línea roja marca el inicio de la cuarentena


#collapse-hide
source = df[df['max_cases'] >= 50000]
source = source[source['cases'] > 0]
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

alt.data_transformers.enable('default', max_rows=None)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('days:Q'),
    alt.Y('cases:Q', scale=alt.Scale(type='log')),
    alt.Color('pais:N')
)

alt.layer(
    base, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='days:Q'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=2).encode(
        text='cases:Q'
    ).transform_filter(label),
    
    base.mark_text(align='left', dx=37, dy=-5, stroke='white', strokeWidth=2).encode(
        text='pais:N'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='cases:Q'
    ).transform_filter(label),
    
    base.mark_text(align='left', dx=37, dy=-5).encode(
        text='pais:N'
    ).transform_filter(label),
    
    data=source
).properties(
    width=700,
    height=400
)


#collapse-hide
source = df[df['max_cases'] >= 50000]
source = source[source['deaths'] > 0]
label = alt.selection_single(
    encodings=['x'], # limit selection to x-axis value
    on='mouseover',  # select on mouseover events
    nearest=True,    # select data point nearest the cursor
    empty='none'     # empty selection includes no data points
)

alt.data_transformers.enable('default', max_rows=None)

# define our base line chart of stock prices
base = alt.Chart().mark_line().encode(
    alt.X('days:Q'),
    alt.Y('deaths:Q', scale=alt.Scale(type='log')),
    alt.Color('pais:N')
)

alt.layer(
    base, # base line chart
    
    # add a rule mark to serve as a guide line
    alt.Chart().mark_rule(color='#aaa').encode(
        x='days:Q'
    ).transform_filter(label),
    
    # add circle marks for selected time points, hide unselected points
    base.mark_circle().encode(
        opacity=alt.condition(label, alt.value(1), alt.value(0))
    ).add_selection(label),

    # add white stroked text to provide a legible background for labels
    base.mark_text(align='left', dx=5, dy=-5, stroke='white', strokeWidth=2).encode(
        text='deaths:Q'
    ).transform_filter(label),
    
    base.mark_text(align='left', dx=37, dy=-5, stroke='white', strokeWidth=2).encode(
        text='pais:N'
    ).transform_filter(label),

    # add text labels for stock prices
    base.mark_text(align='left', dx=5, dy=-5).encode(
        text='deaths:Q'
    ).transform_filter(label),
    
    base.mark_text(align='left', dx=37, dy=-5).encode(
        text='pais:N'
    ).transform_filter(label),
    
    data=source
).properties(
    width=700,
    height=400
)